Using epigenetic clocks to investigate changes in the age structure of critically endangered Māui dolphins

Abstract The age of an individual is an essential demographic parameter but is difficult to estimate without long‐term monitoring or invasive sampling. Epigenetic approaches are increasingly used to age organisms, including nonmodel organisms such as cetaceans. Māui dolphins (Cephalorhynchus hectori maui) are a critically endangered subspecies endemic to Aotearoa New Zealand, and the age structure of this population is important for informing conservation. Here we present an epigenetic clock for aging Māui and Hector's dolphins (C. h. hectori) developed from methylation data using DNA from tooth aged individuals (n = 48). Based on this training data set, the optimal model required only eight methylation sites, provided an age correlation of .95, and had a median absolute age error of 1.54 years. A leave‐one‐out cross‐validation analysis with the same parameters resulted in an age correlation of .87 and median absolute age error of 2.09 years. To improve age estimation, we included previously published beluga whale (Delphinapterus leucas) data to develop a joint beluga/dolphin clock, resulting in a clock with comparable performance and improved estimation of older individuals. Application of the models to DNA from skin biopsy samples of living Māui dolphins revealed a shift from a median age of 8–9 years to a younger population aged 7–8 years 10 years later. These models could be applied to other dolphin species and demonstrate the ability to construct a clock even when the number of known age samples is limited, removing this impediment to estimating demographic parameters vital to the conservation of critically endangered species.


| INTRODUC TI ON
Determining the ages of individuals is critical for understanding the dynamics of a population and for informing effective conservation and management actions. However, age is difficult to assess in wild animal populations, particularly those with life spans longer than typical field efforts. Additionally, age can rarely be determined by appearance and instead requires samples obtained invasively or lethally. This is counterproductive for species of conservation concern. For toothed whales (dolphins and porpoises) age is typically estimated by counting growth layer groups in teeth extracted from a living or dead individual (Bowen & Northridge, 2010). The technique is not without biases though, as tooth wear can lead to underestimation of chronological age (Bowen & Northridge, 2010;Hohn, & Fernandez, 1999). Age can also be inferred from sighting histories of naturally marked individuals, but this is only possible for wellobserved populations in coastal habitats (Hammond et al., 2021).

Molecular aging is now largely based on DNA methylation (De
The ratio of methylation to nonmethylation at these CpG sites can be positively or negatively correlated with the chronological age of individuals in species ranging from mice to humans (e.g., Horvath, 2013; Thompson et al., 2018), including nonmodel organisms such as cetaceans (Beal et al., 2019;Bors et al., 2021;Peters et al., 2022;Robeck, Fei, Haghani, et al., 2021a). Multi-species clocks are also developed to examine trends in aging across taxa (e.g., Parsons et al., 2023;Robeck, Fei, Haghani, et al., 2021a). Penalized linear regression models have been constructed from CpG sites to develop "epigenetic clocks," relating percent methylation at informative sites to derive an estimate of age (DNAm, Horvath, 2013, Horvath & Raj, 2018. Alternatively, age can be estimated based on methylation at a small number of targeted CpG sites, as opposed to surveying the genome with bead-based arrays (e.g., Beal et al., 2019;Polanowski et al., 2014).
Given that clock models can be applied to small samples of skin or blood collected during routine field efforts, there is considerable promise for developing minimally invasive estimates of ages for populations of conservation concern.
Hector's and Māui dolphins (Cephalorhynchus hectori hectori [Van Bénéden 1881]) and (C. h. maui [Baker et al., 2002]) are endemic to the coastal waters of Aotearoa New Zealand. The Māui dolphin is the world's rarest marine dolphin, classified as critically endangered by the International Union for the Conservation of Nature (IUCN) and nationally critical in the New Zealand Threat Classification System Reeves et al., 2013). Fisheries closures have provided greater protection from the long-standing threat of bycatch (Department of Conservation & Fisheries New Zealand, 2021).
A newer concern is the threat of disease associated with increasing prevalence of Toxoplasma gondii Roberts et al., 2021;Roe et al., 2013). Recent boat-based and genetic capture-recapture surveys have estimated the current population size of Māui dolphins at 54 individuals aged 1 year or older, and an effective population size (N e ) of 35 (Constantine et al., 2021). Skin biopsy samples collected during routine survey efforts, as well as samples from dead beachcast and bycaught dolphins have resulted in a growing database of individually genotyped dolphins, and an archive of extracted DNA to test in the development of an epigenetic clock.
Here, we developed and validated two sets of epigenetic clocks for Māui and Hector's dolphins based on DNA methylation data and a training data set of individuals aged by teeth growth layer groups. We also leveraged preexisting DNA methylation training data from skin collected from beluga whales (Delphinapterus leucas, Pallas 1776) to develop a joint beluga/dolphin clock, contributing to similar multi-species clocks for cetaceans (Parsons et al., 2023;Robeck, Fei, Lu, et al., 2021b). We then applied the clocks to generate DNAm age estimates from biopsy samples of living Māui dolphins to determine the age structure of the population during two survey periods. Based on observations during boat-based surveys and genetic capture-recapture data collection (Constantine et al., 2021), we hypothesize that the population of Māui dolphins has shifted to a younger median age over recent survey years. The models developed here advance our understanding of epigenetic clocks for aging cetaceans, particularly in the context of contributing to improved conservation and management (Beal et al., 2019;Bors et al., 2021;Horvath et al., 2022;Peters et al., 2022;Polanowski et al., 2014;Robeck, Fei, Haghani, et al., 2021a). Critically, this approach shows promise for the development and application of epigenetic clocks from species that are elusive or logistically challenging to study, and those with small population sizes, which may not meet suggested sampling protocols (e.g., Mayne et al., 2021 Betty et al. (2022). In brief, teeth were decalcified, sectioned, and growth layer groups were counted following the protocol of Slooten (1991) as modified by Duignan et al. (2004). Sections were counted by two readers, and a consensus age estimate was reached. Additionally, age estimates were assigned to one of four categories based on confidence in the age estimate and other tooth attributes, such as missing centroids or damaged growth layer groups. For the purposes of model building and performance, we selected two subsets from those provided in Betty et al. (2022): a "strict" subset (n = 31, Figure 1a) of individuals with high confidence in age estimates and a "relaxed" subset (n = 48, Figure 1b) of individuals with at least a minimum age estimate (Table S1)

| DNA extraction and profiling
Genomic DNA was extracted from skin samples using a modified phenol-chloroform protocol for small samples (Baker et al., 1994).
Extracted DNA was treated with RNAse A (1.4 μL of 1 mg/mL to samples of 140 μL for 30 min at room temperature) and purified using a Zymo PCR Inhibitor Removal Kit (Zymo Research Corp.). DNA concentrations were measured on a Qubit 4 fluorometer (Thermo Fisher). Sex was identified via a multiplex PCR of marker sets standard for Māui and Hector's dolphins (Hamner et al., 2017). PCR products were visualized by agarose gel electrophoresis to confirm sexes (for necropsied individuals) or assign sexes (for biopsy individuals).
Subspecies for all samples were confirmed by amplification and sequencing of approximately 700 bp at the 5′ end of the mtDNA control region . Sequences were trimmed to align to a 360 bp reference sequence for the diagnostic 'G' haplotype for Māui dolphins or one of the many Hector's dolphin haplotypes (Constantine et al., 2021;Hamner et al., 2012).
Details about individual identification of Māui and Hector's dolphins via a panel of microsatellites have been reported previously (Baker et al., , 2016Constantine et al., 2021;Oremus et al., 2012). In brief, individuals were genotyped at between 14 and 26 loci, depending on when samples were collected and genotyped. Each locus was amplified individually, then coloaded with up to five other loci amplified from the same individual for sizing.
Each amplification included a negative control to detect contamination and up to seven internal control samples to standardize allele binning with previous genotyping runs and to estimate genotyping error. Microsatellite genotypes were compared within and across years using CERVUS 3.0.7 (Kalinowski et al., 2007). Samples with identical genotypes were considered resamples of the same individual. were then removed from further analysis. Fluorescence at the terminal nucleotide was read by an Illumina iScan machine and saved as idat files. Raw methylation data were normalized using the SeSAMe pipeline (Zhou et al., 2018), providing an estimate of methylation at each probe (Beta values, ranging from 0 to 1, with zero indicating no methylation occurred), and a p-value for the confidence in the methylation estimate. We ran two separate logistic models with cv.glmnet() to investigate the potential confounding effects of subspecies and tissue sources on clock models. Specifically, we were concerned that most of the training individuals were dead beachcast Hector's dolphins, while most of the test individuals were derived from biopsy samples of live Māui dolphins. Subspecies and tissue sources were encoded in binary, where "1" indicated a Māui dolphin or biopsy tissue source and "0" indicated a Hector's dolphin or beachcast tissue source. Unlike the clock models described above where alpha was optimized for model performance, the parameter was instead set at 0.5 for these tests. The CpG sites selected for these models were inspected with the match() function in R to determine if any sites diagnostic of subspecies or tissue sources were included in candidate clock models.

| Quality control and molecular clock construction
To leverage existing methylation data and potentially improve age estimation for older Māui and Hector's dolphins, we also constructed a set of models that included skin methylation data from beluga whales collected in Cook Inlet, Alaska, USA Horvath et al., 2021) following the conventions of similar multispecies clock models (e.g., Parsons et al., 2023;Robeck, Fei, Lu, et al., 2021b). The training individuals from Bors et al. (2021, n = 67) were included with both the strict and relaxed subsets of Māui and

| Genomic location of model CpGs
CpG probe locations in the human or mouse genomes are known from methylation array design (Arneson et al., 2022). with NCBI BLAST (Altschul et al., 1990;Johnson et al., 2008), as this is the closest Māui dolphin relative with an annotated genome.

| Single CpG correlations with known ages
The majority of retained CpG sites did not have a significant correlation coefficient with age in either calibration subset (Table 1).
Pearson's correlation analyses with age for the strict subset of individuals found 4748 CpG sites with a significant age correlation,  overparametrizing the relationship relative to other candidate models, and had a large correlation between the estimated tooth ages and the DNAm ages (Figure 2a). A LOOCV analysis with the strict calibration data set confirmed that this was the optimal choice (minimum 10 significant p-values for CpG sites, α = 0.9, Table 3, Figure 2b) as this also optimized the age correlation and age errors relative to other CpG subsets and values of alpha (Table S3).  Table S6). The LOOCV analysis indicated that this model had an age correlation of .90 and a median age error of 3.08 years (Table 3; Table S7).

TA B L E
The best-performing models had some CpG sites in common with the previously published beluga-specific and OEAC models Robeck, Fei, Lu, et al., 2021b). No sites were overlapping between the beluga-specific and Māui/Hector's clock models (Table S8). Two CpG sites in the species-specific Māui/Hector's clock overlapped with the OEAC. Unsurprisingly, 15 CpG sites in the beluga-specific clock overlapped with the joint beluga/dolphin clock. One CpG site, cg15809488, was found in the OEAC, belugaspecific, and joint beluga/dolphin clock.

| Estimated age of living dolphins
Application of the two best-performing models to the biopsy samples from living dolphins provided estimated ages between −0.5 and F I G U R E 2 Scatterplots of the relationship between age estimated from tooth growth layer groups and from methylation data for the "strict" training set of Māui and Hector's dolphins for alpha = 0.9 for the (a) species-specific clock model and (b) the leave-one-out cross-validation analysis. The solid line is the modeled relationship between tooth age and DNAm age. Males are indicated by circles and females by triangles.   [8.28, 9.58]). The age intervals for serially sampled individuals were generally in the correct direction (e.g., older animals were older from samples 1 to 2), but intervals and absolute ages tended to be underestimated with both clock models.

| Genomic location of model CpGs
Half of the eight CpG sites in the Māui/Hector's clock were located within genes found in the bottlenose dolphin genome (Table S10).
The GO enrichment analysis indicated that these four CpG sites were associated with genes linked to protein or mRNA binding.
These results should be considered preliminary and may change upon publication, annotation, and analysis of a Māui or Hector's dolphin genome. The other four CpG sites in the clock model were 2000-200,000 bp away from the closest annotated gene.

| Māui dolphin age structure
Between the two survey periods of 2015-2016 and 2020-2021, the estimated age distribution of Māui dolphins shifted to younger individuals (Figure 3c-f). With the Māui/Hector's clock, the mean estimated dolphin age was 8.5 years in 2015) and decreased to 7.4 years in the 2020-2021 survey (95% CI 6.72, 8.05). Likewise, with the joint beluga/dolphin clock, the mean age between the two survey periods decreased from 9.7 to 8.5 years (95% CI 2015: 8.74, 10.53, 95% CI 2020

| DISCUSS ION
The models presented here reveal the value of multi-species clocks for a critically endangered species with limited sample sizes for age calibration. Clocks applicable to multiple species have also been developed for other odontocetes (Robeck, Fei, Lu, et al., 2021b) and long-lived cetaceans (Parsons et al., 2023). Compared to these recent clocks, the models we developed have comparable or larger age correlations, comparable or smaller median age errors, and produced more realistic age estimates. For example, using the OEAC (Robeck, Fei, Lu, et al., 2021b), the training sets of Māui and Hector's dolphins had DNAm ages overestimated relative to estimated tooth ages and the species-specific clock presented here (Hernandez et al., 2022, Table S11, Figure S2). This consistent overestimation could be a function of the species used in the OEAC training data, most of which have life spans comparable to, or decades longer than, those of Māui and Hector's dolphins (estimated longevity of 20 years, Slooten, 1991).
The accuracy and utility of epigenetic clock models are dependent on the available training information. In a review of best practices, Note: Mean age error (mae), median age error (medae), r 2 for the regression, p-value for the regression, regression slope, and y-intercept for the regression.

TA B L E 3
Statistics for the Māui/ Hector's dolphin and joint beluga/dolphin epigenetic clock models and leave-oneout cross-validation (LOOCV).
similar to the estimated tooth ages, the joint beluga/dolphin clock estimates were more consistent with those from sighting histories and genetic capture-recapture. This further supports the recommendation to include individuals across the range of potential ages in the training dataset (Mayne et al., 2021 Hector's dolphin research (Slooten, 1991;Slooten & Lad, 1991 Allee effects associated with genetics, social structure, reproduction and predation, or interactions between multiple effects (e.g., Berec et al., 2007;Frère, Krützen, Mann, et al., 2010a;Gascoigne et al., 2009;Stephens & Sutherland, 1999). For highly social species like dolphins, it is important to know the age of sexual and social maturity, that is, the age at first reproduction, as these can differ by several years, and whether there is variation in reproductive success by individual and/or age class (Frère, Krützen, Kopps, et al., 2010b;Henderson et al., 2014). Younger animals also may not yet have learned the social cues or behaviors necessary to successfully reproduce.

ACK N OWLED G M ENTS
Biopsy samples were collected in collaboration with the New Zea-

CO N FLI C T O F I NTER E S T S TATEM ENT
SH is a founder of the nonprofit Epigenetic Clock Development

B EN EFIT-S H A R I N G S TAT E M E N T
Dolphin samples were collected with the permissions and in collaboration with mana whenua (local Māori who are kaitiaki/guardians of the Māui dolphins) and this has been included in the Methods and